Head to head · Open-weight models you run yourself · October 2026 research run
DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B
Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
DeepSeek-V3.2DeepSeek · Open-weight model
C58.6/100#65 of 100 open-weight models
Swift 1.5 Qwen3.8-27BUkisAI · Open-weight model
C61/100#49 of 100 open-weight models
Which one, for what
Best for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB
Best for Agents on a 24 to 32 GB GPU or Mac that want Qwen3.8-27B's skills with shorter thinking, for teams under US$1M revenue or research.
Ahead on
- Reach, 80 against 60
- Documentation and upkeep, 43 against 34
Watch for
Commercial use above US$1M gross annual revenue needs a separate licence
Score by category
| Category | Weight this run | DeepSeek-V3.2 | Swift 1.5 Qwen3.8-27B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 80 | Swift 1.5 Qwen3.8-27B +20 |
| Risk | 20%20 | 50 | 50 | even |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 34 | 43 | Swift 1.5 Qwen3.8-27B +9 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58.6/100 | C 61/100 |
Facts side by side
| Fact | DeepSeek-V3.2 | Swift 1.5 Qwen3.8-27B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue with a separate Swift Enterprise License above it. Built on Qwen3.8-27B, whose Apache 2.0 still governs the base |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-12-01 | 2026-09-24 |
| Terms last updated | no document linked | |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.3/5 (4) | 2.3/5 (4) |
Verdicts
DeepSeek-V3.2
DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.
Swift 1.5 Qwen3.8-27B
A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.
Before you call either
DeepSeek-V3.2
- Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
- It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
- Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
- Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
- Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
- Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens
Swift 1.5 Qwen3.8-27B
- Quickest start is
llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector - On our reference machine BF16 fits,
vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - The template is Qwen3.8-27B's, so
chat_template_kwargswithreasoning_effortlow, medium or xhigh (default) andenable_thinkingfalse work as on the base - UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
- The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
- Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
- MTP self-speculative decoding is available with the base's head, flags on the card
Questions
Which is better for AI agents, DeepSeek-V3.2 or Swift 1.5 Qwen3.8-27B?
Swift 1.5 Qwen3.8-27B scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 2 of 4 scored categories.
Other comparisons with DeepSeek-V3.2 or Swift 1.5 Qwen3.8-27B
- Agents-A1-4B vs DeepSeek-V3.2
- Agents-A1-4B vs Swift 1.5 Qwen3.8-27B
- Apertus v1.5 8B vs DeepSeek-V3.2
- Apertus v1.5 8B vs Swift 1.5 Qwen3.8-27B
- Apodex-1.1-mini vs DeepSeek-V3.2
- Apodex-1.1-mini vs Swift 1.5 Qwen3.8-27B
- BigBang-v1 vs DeepSeek-V3.2
- BigBang-v1 vs Swift 1.5 Qwen3.8-27B
- Cosmos-Reason2-2B vs DeepSeek-V3.2
- Cosmos-Reason2-2B vs Swift 1.5 Qwen3.8-27B
- Darwin-36B-Opus vs DeepSeek-V3.2
- Darwin-36B-Opus vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V3.2 vs DeepSeek V4.1 Flash
- DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731
- DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 12B IT
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 31B IT
- DeepSeek-V3.2 vs Gemma 4 E2B IT
- DeepSeek-V3.2 vs Gemma 4 E4B IT
- DeepSeek-V3.2 vs GLM-4.5-Air
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs GLM-5.3-Flash
- DeepSeek-V3.2 vs gpt-oss-120b
- DeepSeek-V3.2 vs gpt-oss-20b
- DeepSeek-V3.2 vs Granite 4.2 3B
- DeepSeek-V3.2 vs Hy3
- DeepSeek-V3.2 vs Inkling
- DeepSeek-V3.2 vs Inkling-Small
- DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- DeepSeek-V3.2 vs Kimi K3
- DeepSeek-V3.2 vs Kolibri-1
- DeepSeek-V3.2 vs Laguna M.1
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs Laguna XS 2.1
- DeepSeek-V3.2 vs LFM2.5-2.6B
- DeepSeek-V3.2 vs Ling-3.0-tiny
- DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V3.2 vs MiniCPM-o 4.5
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek-V3.2 vs MiniMax-M2.5
- DeepSeek-V3.2 vs MiniMax-M2.7
- DeepSeek-V3.2 vs Muse Glimmer 30B
- DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 4B
- DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B
- DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B
- DeepSeek-V3.2 vs occamy-1.0
- DeepSeek-V3.2 vs Olmo 3 7B Think
- DeepSeek-V3.2 vs Ornith-1.5-35B-A3B
- DeepSeek-V3.2 vs Ornith-1.5-397B
- DeepSeek-V3.2 vs Ornith-1.5-9B
- DeepSeek-V3.2 vs Qwen3-30B-A3B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3-4B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3.5-0.8B
- DeepSeek-V3.2 vs Qwen3.5-122B-A10B
- DeepSeek-V3.2 vs Qwen3.5-2B
- DeepSeek-V3.2 vs Qwen3.5-397B-A17B
- DeepSeek-V3.2 vs Qwen3.5-4B
- DeepSeek-V3.2 vs Qwen3.5-9B
- DeepSeek-V3.2 vs Qwen3.6-27B
- DeepSeek-V3.2 vs Qwen3.6-35B-A3B
- DeepSeek-V3.2 vs Qwen3.8-27B
- DeepSeek-V3.2 vs Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs Qwen3-Coder-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-Coder-Next
- DeepSeek-V3.2 vs Qwen3-Omni-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-2B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Qwopus3.8-27B-Flash-V2
- DeepSeek-V3.2 vs SmolLM3-3B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)
- DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V4-Flash-0731 vs Swift 1.5 Qwen3.8-27B
- DiffusionGemma 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 12B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 26B A4B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 31B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 E2B IT vs Swift 1.5 Qwen3.8-27B
- Gemma 4 E4B IT vs Swift 1.5 Qwen3.8-27B
- GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B
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- GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B
- GLM-5.3 vs Swift 1.5 Qwen3.8-27B
- gpt-oss-120b vs Swift 1.5 Qwen3.8-27B
- gpt-oss-20b vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B
- Hy3 vs Swift 1.5 Qwen3.8-27B
- Inkling-Small vs Swift 1.5 Qwen3.8-27B
- Inkling vs Swift 1.5 Qwen3.8-27B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Swift 1.5 Qwen3.8-27B
- Kimi K3 vs Swift 1.5 Qwen3.8-27B
- Kolibri-1 vs Swift 1.5 Qwen3.8-27B
- Laguna M.1 vs Swift 1.5 Qwen3.8-27B
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna XS 2.1 vs Swift 1.5 Qwen3.8-27B
- LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B
- Ling-3.0-tiny vs Swift 1.5 Qwen3.8-27B
- MiMo-V2.6 Distill Qwen 9B vs Swift 1.5 Qwen3.8-27B
- MiniCPM-o 4.5 vs Swift 1.5 Qwen3.8-27B
- MiniCPM5-2B vs Swift 1.5 Qwen3.8-27B
- MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B
- MiniMax-M2.7 vs Swift 1.5 Qwen3.8-27B
- Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3.5 Lightning 30B A3B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano 4B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B
- occamy-1.0 vs Swift 1.5 Qwen3.8-27B
- Olmo 3 7B Think vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B
- Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B
- Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B
- Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-2B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-4B vs Swift 1.5 Qwen3.8-27B
- Qwen3.5-9B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B
- Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-27B
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B
- SmolLM3-3B vs Swift 1.5 Qwen3.8-27B
- Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B
- Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/deepseek-v3-2-vs-swift-1-5-qwen3-8-27b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/deepseek-v3-2.json·/api/v1/tools/swift-1-5-qwen3-8-27b.json - From a terminal
anchor compare deepseek-v3-2 swift-1-5-qwen3-8-27b(the CLI) - Over MCP
compare_tools {"a": "deepseek-v3-2", "b": "swift-1-5-qwen3-8-27b"}at/mcp, no key